An analysis showing that structured outputs can sometimes perform worse than unstructured ones in certain tasks for different LLM models, emphasizing the importance of testing both approaches.
Concatenate a directory full of files into a single prompt for use with LLMs
Google introduced Jules, an AI-powered coding assistant built on their Gemini 2.0 platform that autonomously fixes bugs and integrates with GitHub's workflow system to speed up software development.
MCP is an open-source standard that enhances interaction between AI systems and various data sources, improving usability, response quality, and security.
Persys is a locally-run device designed to function as a second brain. The repository includes the backend server (Linux only) and the Electron-based desktop application for accessing the server.
David Ferrucci, the founder and CEO of Elemental Cognition, is among those pioneering 'neurosymbolic AI' approaches as a way to overcome the limitations of today's deep learning-based generative AI technology.
Snowflake recently announced the launch of Arctic Embed L 2.0 and Arctic Embed M 2.0, two small and powerful embedding models tailored for multilingual search and retrieval. The models are available in medium and large variants, with the medium model incorporating 305 million parameters and the large variant with 568 million parameters. Both models support context lengths of up to 8,192 tokens. They demonstrate high-quality retrieval across multiple languages and excel in benchmarks like MTEB and CLEF.
Learn how to build Llama 3.2-Vision locally in a chat-like mode, and explore its Multimodal skills on a Colab notebook.
ServiceNow releases AgentLab, an open-source Python package for developing and evaluating web agents, addressing challenges in agent development such as poor scalability and difficulty in conducting reproducible experiments.
This article discusses the development of multimodal Retrieval Augmented Generation (RAG) systems which allow for the processing of various file types using AI. The article provides a beginner-friendly guide with example Python code and explains the three levels of multimodal RAG systems.
